Khimya Khetarpal

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 239 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 397 citations into it
• Reinforcement Learning — 230 citations into it
• Computer Vision and Pattern Recognition — 94 citations into it
• Management Science and Operations Research — 71 citations into it; #11,631 of 11,946 by trust here, #8,906 by citations
• Meta & Continual Learning — 70 citations into it
• Cognitive Neuroscience — 63 citations into it
• Computational Theory and Mathematics — 50 citations into it
• Representation & Self-Supervised Learning — 34 citations into it

Whom Khimya Khetarpal trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. J. Urgen Schmidhuber
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Andrew Barto
7. Alex Krizhevsky
8. Yann LeCun
9. Ronald J. Williams
10. Ilya Sutskever

Who trusts Khimya Khetarpal overall

1. Martin Klissarov
2. Shivam Goel
3. Zafarali Ahmed
4. Milena Rmus
5. Qingfeng Lan
6. Kevin Lu
7. Doina Precup
8. Matthew Riemer
9. Sam Powers
10. Gyan Tatiya

Papers from Khimya Khetarpal's perspective

Ordered by how strongly the researchers Khimya Khetarpal trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Towards Continual Reinforcement Learning: A Review and Perspectives (2020) — 437 citations
• Environments for Lifelong Reinforcement Learning (2018) — 8 citations
• Options of Interest: Temporal Abstraction with Interest Functions (2020) — 52 citations
• Sequoia: A Software Framework to Unify Continual Learning Research (2021) — 25 citations
• Value Preserving State-Action Abstractions (2020) — 59 citations
• What can I do here? A Theory of Affordances in Reinforcement Learning (2020) — 64 citations
• RE-EVALUATE: Reproducibility in Evaluating Reinforcement Learning Algorithms (2018) — 29 citations